Journal of Diabetes最新文献

We thank the authors for their insightful comments and for recognizing that our manuscript provides valuable details about behavioral and genetic factors affecting the risk of obesity in people with type 2 diabetes (T2D) [1].

Firstly, as mentioned in our article, there may be recall bias based on patient self-reported sleep duration, and objectively measuring habitual sleep duration using actigraphy can provide more reliable data. However, considering its simplicity, practicality, and correlation with instrument measurement results, patients' self-reported sleep duration is still internationally recognized and widely used in population-based studies [2, 3].

Secondly, the relationship between sleep, diabetes and obesity is really complex and challenging. As mentioned by the authors, most exploratory experiments on sleep are temporary, especially for sleep deprivation. There are interactions among sleep, diabetes and obesity that impact cardiovascular and metabolic health, just like the intertwined trio. To avoid the potential impact of sleep related diseases, we excluded patients who reported implausible values of sleep duration (i.e., < 3 or > 12 h/night), use of sleeping aids, or psychiatric medications at baseline. It should be pointed out that the above efforts cannot completely eliminate the potential impact of sleep disorders on our study results, and how to reduce their potential effects is also one of the key considerations in our future research. The Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS) are indeed effective tools.

As we mentioned in the article, we acknowledge that “although we have adjusted for several covariates, there are potential confounders that could influence the results, such as dietary habits, physical activity, socioeconomic biases, and glucose-lowering medications, which we did not include in this study, and further research is needed to strengthen our understanding of these complex associations”, when it comes to both clinical and genetic analysis. In addition, a more complex and precise polygenic risk score (PRS) model for body mass index (BMI) is also under consideration.

In conclusion, we thank the authors for the wise and valuable comments and suggestions. We hope these clarifications address the issues raised and we intend to use more complex and appropriate models to explore their associations in future research.

The authors declare no conflicts of interest.

The glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1RAs) are a class of medications primarily developed for the management of type 2 diabetes (T2D) and are increasingly being used for various other health effects. Here's a simplified overview of their history, effects, and future prospects:

Glucose-dependent insulinotropic polypeptide (GIP) and then GLP-1 were identified in the 1970s as peptides potentiating the insulin secretory response to nutrient ingestion. In the early 1980s, the proglucagon amino acid sequence and gene were mapped, leading to the recognition of the common derivation of GLP-1 and GLP-2, produced in the L-cells of the distal small intestine, while GIP is produced in the K-cells of the proximal small intestine. The first therapeutically used GLP-1RA was isolated as exendin-4 from the saliva of a venomous lizard, Heloderma suspectum (the “Gila monster”), in 1992, with initial preclinical studies reported in 1996; although FDA approval for its use in treatment of T2D as exenatide was not granted until 2005. The next GLP-1RA used therapeutically was liraglutide, receiving approval in 2010, with dulaglutide receiving approval in 2014, semaglutide approval in 2017, and tirzepatide (a combined agonist of GLP-1 and GIP) approval in 2022; FDA approvals of liraglutide, semaglutide, and tirzepatide for treatment of obesity were granted in 2014, 2021, and 2023, respectively.

Both the GLP-1 and GIP receptor agonists enhance insulin production when glucose levels are elevated. This physiological insulin-secretory effect contrasts with the action of the older sulfonylureas, which activate the ATP-sensitive potassium channels of the pancreatic beta cells, mimicking the cellular process that physiologically acts to link insulin production to the beta cell's energy state. The consequent increase in endogenous insulin production leads the GLP-1RAs to have glucose-lowering action comparable to that of daily treatment with basal insulin [1]; with weight loss rather than weight gain and with lower likelihood of hypoglycemia [2, 3].

In addition to their glycemic benefits, GLP-1RAs are associated with improvement in mortality and in CV and renal outcomes in clinical trials [4, 5], both in people having and not having T2D [6], as well as in real-world studies of people with T2D [7] and of people with obesity without T2D [8]. In a study from the US Veteran's Affairs hospitals with mean followup of nearly 4 years comparing more than 200 000 persons receiving GLP-1RA with more than 1.2 million receiving usual care, benefits of GLP-1RAs included reduction in risk of stroke, myocardial infarction, pulmonary embolism, phlebitis, heart failure, hepatic failure, chronic kidney disease, bacterial infections, postprocedural respiratory complications, aspiration pneumonitis, chronic obstructive pulmonary disease, pneumonia and respiratory failure, a

This commentary discusses the study “Sleep Phenotypes, Genetic Susceptibility, and Risk of Obesity in Patients with Type 2 Diabetes: A National Prospective Cohort Study” by Xi et al. The study provides a perceptive investigation of how metabolic characteristics and genetic variables interact to affect body mass index (BMI) in people with T2D. It has some limitations, including reliance on self-reported sleep data and lack of in-depth sleep quality assessment. We highlight the importance of objective sleep data, detailed assessments of sleep quality, and advanced polygenic risk models in future research to better understand these relationships and inform precision diabetes care.